Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications

Tuberculosis is an airborne infectious disease caused by Mycobacterium tuberculosis. BCG is the only approved vaccine. However, it has limited global efficacy. Pathogens could affect the transcription of host genes, especially the ones related to the immune system, by inducing epigenetic modifications. Many proteins of M. tuberculosis were found to affect the host’s epigenome. Nine proteins were exploited in this study to predict epitopes to develop an mRNA vaccine against tuberculosis. Many immunoinformatics tools were employed to construct this vaccine to elicit cellular and humoral immunity. We performed molecular docking between selected epitopes and their corresponding MHC alleles. Thirty epitopes, an adjuvant TLR4 agonist RpfE, constructs for subcellular trafficking, secretion booster, and specific linkers were combined to develop the vaccine. This proposed construct was tested to cover 99.38% of the population. Moreover, it was tested to be effective and safe. An in silico immune simulation of the vaccine was also performed to validate our hypothesis. It also underwent codon optimization to ensure mRNA’s efficient translation once it reaches the cytosol of a human host. Furthermore, secondary and tertiary structures of the vaccine peptide were predicted and docked against TLR-4 and TLR-3.Molecular dynamics simulation was performed to validate the stability of the binding complex. It was found that this proposed construction can be a promising vaccine against tuberculosis. Hence, our proposed construct is ready for wet-lab experiments to approve its efficacy.

Tuberculosis is an infectious disease caused by the pathogen Mycobacterium tuberculosis. In the World Health Organization (WHO) 2020 report, approximately 10 million people were diagnosed with TB and 1.2 million deaths during 2019 1 . Primarily, M. tuberculosis infects macrophages. However, they also have neutrophils and dendritic cells as hosts. Once M. tuberculosis manages to enter the host, they may stay still for a long time or get removed by the immune system. It depends on two factors; the strain virulence of Mycobacteria and the milieu of the infected macrophages. Many phagocytic vacuoles get fused with the lysosome. Hence the bacterial molecules reside in the formed phagolysosome. Proteolysis of pathogens by phagolysosomes can output peptides that can be antigenic or immunogenic 2 .
There are many available first and second lines of treatments for TB 3 . However, there is a challenge of multidrug resistance to TB drugs 4 . The only approved TB vaccine is Bacillus Calmette-Guérin (BCG). The route of injection is intradermal. However, this vaccine is limited in use due to its variability in efficacy worldwide 5,6 . Research on vaccine development was advanced through the years. The conventional approaches can successfully induce immunogenicity and stocking durable protection. Examples include living attenuated and inactivated bacteria and subunit vaccines 7 . However, many novel methods such as peptide-based vaccines and DNA vaccines showed promising opportunities for developing scalable and rapid vaccines. Nevertheless, peptide-based vaccines showed a low immunogenicity index 8 , while pDNA showed a risk of insertional mutagenesis when used inside DNA vaccines 9 . However, mRNA vaccines were found to be more efficacious than DNA vaccines as it was found that mRNA vaccines can potentially address these concerns of safety and efficacy. It is believed that mRNA therapeutics are non-infectious entities because of their lack of genomic integration and replication except for rare cases of recombination between single-stranded RNA molecules 10 . There is no need for mRNA sequences to pass through the nuclear envelope. Therefore translation for the sequences happens outside the www.nature.com/scientificreports/     Evaluation of antigenicity, allergenicity, toxicity, and physicochemical properties of the vaccine construct. We tested the vaccine construct for its antigenicity, allergenicity, and toxicity using the Vaxi-Jen, ANTIGENpro, AllerTop, and ToxinPred servers. We also calculated the physicochemical properties of the vaccine using the ProtParam server ( Table 6). The vaccine stood to be antigenic, non-allergenic, and non-toxic. Moreover, all its physicochemical properties indicated that the vaccine is thermally stable. The Grand average of hydropathicity (GRAVY) was measured to be -0.300, which indicated the hydrophilic nature of the vaccine. Based on these results, this multi-epitope mRNA vaccine construct can be a potential vaccine candidate.

Population Coverage Prediction.
We measured the combined global population coverage of the 23 epitopes for their corresponding 65 alleles via the IEDB Population Coverage tool. The vaccine found that it would cover around 99.38% of the world.
In silico immune simulation of response against the vaccine. We used three injections of the vaccine to simulate the immune response (Fig. 3). The second and third responses were higher than the primary ones. The produced immunoglobulin (IgM) was higher than IgG. After antigen reduction, the levels of immunoglobulins were high. This increase could indicate that immune memory could emerge from exposure to the antigen and thus indicate practiced immunity. Isotype switching and memory formation of the B cell population is evidenced due to the presence of B-cell isotypes for a prolonged duration of time. Moreover, there was an increase in CTL and HTL cells with memory generation. Furthermore, macrophage activity was enhanced, while dendritic cell activity was stable. Additionally, levels of IFN-γ and IL-2 were also increased. The epithelial cells were increased, which are components of innate immunity. Ultimately, the Simpson index (D) is low, which points out a difference in the immune response. www.nature.com/scientificreports/  www.nature.com/scientificreports/ Codon Optimization of the mRNA construct. Codon optimization tools are needed to enhance the translation of the mRNA vaccine once inside host cells. Therefore, we used the GenSmart Codon Optimization tool (GS) for efficient expression in human cells. The CDS length of the mRNA was 1896 nucleotides. We used the rare codon analysis tool from GS to assess the quality of the optimized construct. The CAI value was estimated to be 0.91 (Fig. 4A). As the CAI is more significant than 0.8, it is acceptable. The optimal percentage of GC content needs to be around 30-70% to indicate that the vaccine sequence will be expressed efficiently in the human host. The average GC percentage of the optimized construct was 64.64% (Fig. 4B). The Codon Frequency Distribution (CFD) was 0% (Fig. 4C). Thus, this figure indicates that no codons could hamper the translation efficiency or function. As, any codons with a value lower than 30 could reduce or stop translational machinery.
Prediction of the secondary structure of the mRNA. The structure of the mRNA vaccine was predicted via the RNAfold server 26 . The free energies of the structure were also assessed using this server. As an input, we used the optimized codons of the construct. We found that the mRNA will be stable when manufactured with minimum free energy (MFE) of the structure of -802.76 kcal/mol (Fig. 4D), and the secondary centroid structure of -650.48 kcal/mol (Fig. 4E).

Prediction and validation of secondary and tertiary structures of the translated construct.
We used the PSIPRED web service to predict the secondary structure of the vaccine 27 . The alpha helices prevailed in the structure (Fig. 5A). Moreover, we predicted the tertiary structure of the peptide using the Robetta server ( Fig. 5B). We then used the PROCHECK server to verify the stereochemical accuracy of the structure. This server considers both the geometry of residues and the whole structure for the prediction. In (Fig. 5C), the Ramachandran plot indicates that around 87.5% of residues were in the most favored regions, 9.6% in the additionally allowed zone, and 1.6% in the generously allowed regions. The overall quality factor was 93.5275. The ProSA-web predicted a negative Z-score (− 9.78) that indicates that the 3D protein model is very consistent (Fig. 5D).

Conformational B-cell epitopes prediction.
The folding of the model protein results in the generation of conformational B-cell epitopes. We used the ElliPro server to predict six discontinuous B-cell epitopes. A total of 335 residues were with a prediction score ranging from 0.515 to 0.809. The 2D and 3D models of the conformational B-cell epitopes are shown in (  In the deformability plot, the amino acids with coiled shapes are presented. Normal mode analysis (NMA) is a computational approach for studying and characterizing protein flexibility. The relationship between the NMA and PDB areas in the uploaded complex is depicted in the B-factor graph (Figs. 7C, 8C). The eigenvalues of the docked complexes are in (Fig. 7D, 8D). To this extent, these results emphasize that the vaccine-receptor complex is with both a low deformation index, stronger stiffness, and more stability. In (Figs. 7E, 8E), the covariance matrix demonstrated the connection between amino acid duplets scattered in dynamical regions. The red color represents the correlated residues, the white represents the anti-correlated amino acid duplets, and the blue represents non-correlated residues. A connecting matrix that represents the elastic network model is employed to classify which atom pairs are connected by springs (Figs. 7F, 8F). Each chain of the complex was found to be with higher stiffness. The darker gray color indicates stiffer regions.

Discussion
Tuberculosis is still a burden on global health. To date, the only approved anti-tuberculosis vaccine is BCG. This vaccine was established in 1921. Despite this fact, no other vaccines have been approved. For young children, it was found effective. However, it showed variable efficacy in adults. This variation was due to genetic variability among strains used to develop the BCG vaccine and media used to culture these strains as these criteria showed differences in immunogenicity. Moreover, prior exposure to non-tuberculous mycobacteria can affect the efficacy of BCG by inducing inhibition to its replication 28,29 . Many anti-tuberculosis vaccines have made their way to clinical trials and phase III trials to boost the effectiveness of the BCG vaccine 30 . However, it is time-consuming and expensive to develop and investigate proposed vaccines for efficacy and safety. Accordingly, immunoinformatics could be a gold approach to engineer vaccines and drugs compared to traditional in vitro and in vivo approaches.
Of the successful stories of using immunoinformatics tools are developing vaccines against rickettsia 31 38 , and many others. They were found to be effective and safe. Of the drawbacks to their usage were their instability due to degradation via RNases that are ubiquitous and innate immunity that can immediately recognize these structures as foreign 39,40 . However, headway was achieved to mRNA therapeutics since then. www.nature.com/scientificreports/ Several in vivo and in vitro studies showed an association between epigenetic modifications and M. tuberculosis during infection. This epigenome regulator role is used by the M. tuberculosis to survive inside the host. Accordingly, innovative strategies to target this role can be exploited to develop either therapeutics or vaccines against this pathogen. These epigenetic modifications affect the genes of the immune system. However, it also affects the "trained immunity" term 24 . This theory indicates the occurrence of memory of the innate immune cells; as a result, quick and robust immunity occurs in secondary exposure to the infection 25 . This profile can be detected among individuals and are heritable for changes among populations 41 .
The main goal to achieve using vaccines is to provide a long-lasting memory. Thus, it is crucial to activating both B and T cells to realize this aspect. Accordingly, the host will efficiently and promptly respond to the infectious agent once encountered in the future 42,43 . However, the success of any vaccine is the proper use of specific www.nature.com/scientificreports/ antigens that are named epitopes 44 . Therefore, it is critical to predicting the epitopes that can elicit both B and T cells to implement in the vaccine construct. The HTL epitopes need to produce cytokines such as IFN-γ, IL-4, and IL-10 45,46 . Once antigen-presenting cells display the epitope for HTLs while using MHC class II molecules, the HTLs excrete these chemokines that play critical roles against pathogens. Once pathogens get eliminated, all immune cells perish except the memory cells 47 . The B cells possess receptors that are considered membranebound immunoglobulins that recognize antigenic epitopes 48 . Subsequently, these cells internalize and process the epitopes to present them to T cells using MHC II. As a result, these features had recognized by the T-cell receptor (TCR) of HTLs 49 . Accordingly, B cells differentiate to plasma cells that secrete antibodies that neutralize invaders and memory cells 49 .
For the previously mentioned reasons to manage the global tuberculosis crisis, we have designed in silico a multi-epitope mRNA vaccine while using the proteins of M. tuberculosis. These proteins affect the host epigenome, especially the genes related to the immune system. Each protein was investigated solely for epitopes that could elicit humoral and cell-mediated immunity. The IEDB is an online database to predict HTL and CTL epitopes from various experimentally derived immune epitopes 50 . The ABCpred was used to predict the linear B-cell epitopes. This server is an online database that performs the prediction using artificial machine learning 51 . The efficacy and safety of the epitopes were screened by analyzing them to be antigenic, non-allergen, and nontoxic using related online servers [52][53][54] . Specific linkers had used to connect between epitopes. The TLR4 agonist (RpfE) was added to the N terminus of the sequence to boost immunity. Ultimately, an immune simulation of the effect of the vaccine had performed to validate that this construct can involve both humoral and cellular responses as investigated. Three injections of the vaccine had used. All investigated proteins were extracted from the M. tuberculosis strain (ATCC 25618 / H37Rv). However, the selected epitopes need to lie in conserved regions of the bacterial genome. Accordingly, an alignment to all variants was performed. The chosen epitopes www.nature.com/scientificreports/ for our vaccine construct have 65 corresponding MHC alleles. However, six selected epitopes were subjected to molecular docking. It is critical to vaccine design that the ligand and receptor are robustly engaged [55][56][57] . Accordingly, molecular docking was performed to the selected epitopes with their corresponding MHC alleles. This tool is crucial to predict the binding affinity and pose during spontaneous bond formation. The lower the energy, the more tightly the receptor is bound to its ligand 58 . Interactions exhibited between the epitope and the pocket of MHC are also studied. Moreover, the vaccine construct also addressed population coverage for both CTL and HTL epitopes. Only the individuals with a specific MHC allele that recognizes the epitope will respond to the vaccine as there are more than a thousand human MHC alleles in the world 59 . Hence, the IEDB population coverage tool was used to predict this coverage. It was found that this vaccine covers almost 99.38% of the population. Several structures were included in the mRNA vaccine to boost translation and stability capacity. Of these are: (1) 5′ m7G cap sequences 60 (2) Poly (A) tail of length between 120-150 bps 61 (3) Globin 5′ and 3′ UTRs that flank ORF of mRNA 62 (4) The stop Codon 63 and (5) the Kozak sequence 64 . Moreover, the tPA secretory signal sequence 65 and MITD sequence 66 to direct it to endoplasmic reticulum were added to improve the efficacy and allocation of the construct. Once it reaches the cytosol, the mRNA will enter the translation machinery to get post-translational modifications, producing an immune reaction (Fig 9). It is also essential to maintain the G: C content of the sequence for more stabilization and protein expression 67 . For administration purposes, mRNA can be encapsulated in a Lipid Nanoparticle (LNP) vector 35,38,68 . The route of administration is a factor to consider. The intramuscular and intradermal routes enhanced protein translation triple times than the intravenous route. www.nature.com/scientificreports/ Intranasal delivery can be an option that needs to be investigated on mice. The half-life of the vaccine is needed to be high, as more persistent antigens result in more immune response. Adjuvants are used to boost immunity while considering both efficacy and safety measures without any risk of undesired activation and inflammatory response in some cases. Here, we used TLR4 agonist RpfE. It was found that mutation of TLR4 in mice results in towering bacterial load when infected with M. tuberculosis 69 . It is known that TLR4 is a receptor involved in recognition of M. tuberculosis and thus activates macrophages and dendritic cells that lead to activation of innate and adaptive immunity 70 . Further, the TLR-3 was docked with the vaccine to examine the capability of the constructed vaccine to bind with TLR on immune cells. The results revealed that the vaccine had a high binding affinity towards TLR-4 and TLR-3. Thus a probability of producing both innate and adaptive immunity. MD simulation was implemented to explore the complex's stability, and the RMSD plot represented the steady binding of the complex. However, the naked mRNA vaccine can be used without an adjuvant 11 . This approach has both positives and drawbacks. It could reduce time and cost, exposing the sequence to be destroyed in vivo. Accordingly, a practical investigation is needed to determine if this step is needed. www.nature.com/scientificreports/ As an appendix to the developing mRNA vaccine, it is essential to consider other factors such as its manufacturing and quality. Moreover, it is crucial to solve all related issues using the mRNA vaccine and ensure Good Manufacturing Practice (GMP). Thus, mRNA can be amplified in vitro using PCR or plasmid DNA with a sitespecific RNA polymerase 71 . Then, purification of mRNA vaccine is performed as all contaminants of doublestranded RNA (dsRNA) are recognized and can induce Pathogen-associated Molecular Patterns (PAMPs) and type I interferon (Fig. 9). Thus, inhibition of translation and degradation of cellular mRNA could occur if this purification step is not performed 72 . The most successful method of purification is chromatography to obtain a purified mRNA of a specific length. This way resulted in robust translation to almost 1000-fold. Moreover, another method named PUREmessenger was used to produce purified mRNA. However, the best-reported method to produce an increase in protein production was when mRNA vaccine was both HPLC-purified and nucleoside modified 72 . For in vivo delivery purposes, a vector is used to deliver the mRNAs into the cytosol. The mRNAs enter the cellular translation machinery and post-translational modifications resulting in folded and fully functional proteins. The tPA secretory signal and MITD sequences direct the peptides to ER and Golgi apparatus for efficient secretion and presentation by MHC molecules (Fig. 9).
The IEDB database was used to predict the epitopes enlisted in the vaccine. This database contains spacious immune epitopes extracted from real experimental data 50 . The C-ImmSim webserver was used to profile the immunity of our designed vaccine. It uses specifically Position-Specific Scoring Metrix (PSSM) to simulate immune reactions. It possesses a collection of 6533 epitopes and 33 sets of human HLA alleles 73 . Essentially, the peptide sequence of the proposed mRNA vaccine was found to be stable, antigenic, non-allergenic, thermostable, and hydrophilic using immunoinformatics tools. It managed to induce an immune response once administered in silico with three injections. It was indicated that it could produce a memory after its exposure-high levels of B and T cells and the production of high levels of IFN-γ and IL-2. IFN-γ indicates cell-mediated immunity, and this chemokine can support B-cell proliferation, Ig isotype switching, and humoral response. Moreover, the activity of dendritic cells and macrophages and the Simson index indicate the generation of memory. Thus it can be concluded that this construct can be an excellent candidate to be considered a vaccine against tuberculosis.
In conclusion, the proposed construct shows desirable physicochemical properties and immunological responses. The performed immune simulation displayed that the vaccine elicited an immune response consistent with our hypothesis. Therefore, it is suggested to thrust forward in using this construct as a potential candidate for in vitro and in vivo studies against M. tuberculosis while using several serological assays to confirm the trigger of response in demand.

Methods
The Pipeline of our research methodology was outlined in Fig. 10. ABCpred, an online server (https:// webs. iiitd. edu. in/ ragha va/ abcpred/ABC_submission.html), was used 51 . The ABCpred predicts linear B-cell epitopes using artificial machine learning. Each chosen protein was submitted individually with a 0.51 threshold. The length of epitopes was selected as 16mer. An overlapping filter was kept on. The top five epitopes of the results were studied further.

Retrieval
HTL epitopes prediction. Specifically, the MHCII server of Immune Epitope Database and Analysis Resources (http:// tools. iedb. org/ mhcii/) was used to predict HTL epitopes 50 . Independently, the fasta format of the amino acid sequences of the included proteins in this study was submitted to the server. NN-align 2.3 (Net MHC II 2.3) was used to predict the epitopes. The full HLA human reference set was utilized. The epitope length was specified to be 15mer. Finally, the results were sorted according to their adjusted ranks. Moreover, INFepitope (http:// crdd. osdd. net/ ragha va/ ifnep itope/ predi ct. php) 75 , IL4pred (https:// webs. iiitd. edu. in/ ragha va/ il4pr ed/ predi ct. php) 76 , and IL-10pred (https:// webs. iiitd. edu. in/ ragha va/ il10p red/ predi ct3. php) 77 servers were used to predict if these predicted epitopes can secrete IFN-γ, IL-4, and IL-10 respectively. All chosen epitopes showed the ability to secrete these cytokines.
CTL epitopes prediction. In the case of predicting CTL epitopes, the IEDB MHC I server (http:// tools. iedb. org/ mhci/) was use 50 . The selected proteins were submitted in fasta format. The ANN 4.0 setting was used to predict 9mer and 10mer epitopes. The complete human HLA reference set was used. Ultimately, the resulting peptides were sorted according to predicted IC50. Only epitopes with an IC50 over 500 were chosen.
Human homology. All  www.nature.com/scientificreports/ possibility of any autoimmunity. All peptides were selected further to be considered as possible non-homologous peptides in the vaccine if they have an E value higher than 0.05 78 .
Prediction of epitope's antigenicity, allergenicity, and toxicity. All selected epitopes were tested for their antigenicity, allergenicity, and toxicity. Antigenicity prediction utilized the VaxiJen web server (http:// www. ddg-pharm fac. net/ Vaxij en/ VaxiJ en/ VaxiJ en. html). The prediction is based on the physicochemical properties of epitopes in an alignment-independent fashion. Bacteria and a threshold of 0.4 were singled out 52 . To predict the allergenicity of epitopes, the AllerTop V.2.0 webserver (http:// www. ddg-pharm fac. net/ AllerTOP) was used 53 . All parameters were kept to default. Ultimately, the ToxinPred server (https:// webs. iiitd. edu.in/ raghava/toxinpred/multi_submit.php) was used to predict and measure the toxicity of epitopes by generating all potential mutants while keeping all parameters to default 54 . For further steps in the research, only the epitopes that were found antigenic, non-toxic and non-allergenic were kept.
Multiple sequence alignment. The NCBI database has been used to obtain all the variants of the selected proteins. The Bioedit 7.2 sequence alignment and analysis program was used to perform the alignment and viewing 79 . Then, we searched if all previously predicted epitopes lie in the proteins' conserved regions.

Molecular docking between T lymphocytes epitopes and MHC alleles.
Some of the extracted T lymphocyte epitopes were evaluated for binding affinity for their corresponding MHC alleles while employing molecular docking simulation. The 3D structures of MHC alleles were downloaded from the RCSB PDB database. Then, PyMOL software was used to process the structures and remove unnecessary ligands 80 . Subsequently, the Swiss-PDB Viewer was used for energy minimization for the structures 81 . In parallel, all selected epitopes for docking were folded into three-dimensional using PEP-FOLD 3.5 server 81 , and energy minimized using the Swiss-PdBViewer. The ClusPro 2.0 server (https:// clusp ro. bu. edu/ login. php) was used to dock each  www.nature.com/scientificreports/ epitope with its corresponding MHC allele and calculate the binding affinity [82][83][84] . The pose and interactions were studied using PyMOL 80 and Discovery Studio 85 , respectively.
Population coverage analysis. The combined population coverage was measured for the selected T lymphocytes epitopes in the vaccine construct and their corresponding MHCI and MHCII alleles using the Population Coverage tool (http:// tools. iedb. org/ popul ation/) in the IEDB database 59 . This measured value depends on the coverage of the MHC alleles that the epitopes in the construct recognize. It is because of the diversity in the distribution of MHC alleles against different geographical and ethnicity around the globe.
Design of vaccine construct. The mRNA vaccine construct was proposed from the N-to C-terminus as the following: 5′ m7GCap-5′ UTR-Kozak sequence-tPA (Signal peptide)-EAAAK Linker-RpfE (Adjuvant)-GPGPG linker-HTL Epitopes-KK-LBL Epitopes-AAY Linker-CTL Epitopes-MITD sequence-Stop codon-3′ UTR-Poly (A) tail. All proposed epitopes were linked through three linkers: AAY, KK, and GPGPG linkers 86 . These linkers exist to separate domains to let them act separately. They are cleavable, flexible, and rigid. An adjuvant Resuscitationpromoting factor (RpfE) (Rv2450c) was used to boost the adaptive immune reaction. In the mRNA vaccine, it is necessary to add a Kozak sequence which also includes a start codon 64 in the ORF and a stop codon 63 . Moreover, two structures were added to the construct, which includes (1) The tissue Plasminogen Activator (tPA) secretory signal sequence (UniProt ID: P00750) in the 5′ region of the construct. This element is a signal sequence to help the secretion of epitopes once translated out of the cell if required 65 . (2) The MHC I-targeting domain (MITD) (UniProt ID: Q8WV92) in the 3′ locus of the mRNA vaccine. This sequence is needed to steer CTL epitopes toward the MHC-I compartment of the endoplasmic reticulum 66 . Moreover, it is crucial for stability purposes to add 5′ cap, 120-150 bases of poly(A) tail 87 , and β globin 5′ and α globin 3′ Untranslated regions (UTRs) to mRNA vaccines 61 . The vaccine construct was named RABA_MARZ_14.5.9.
Prediction of antigenicity, allergenicity, toxicity, and physicochemical properties of the vaccine construct. The VaxiJen 2.0 52 and ANTIGENpro servers 88 were used to predict the antigenicity of the vaccine construct. This prediction is crucial as the antigenicity of an antigen can elicit immune response and form memory cells. In which, VaxiJen 2.0 performs predictions based on several physicochemical properties of the vaccine, while the ANTIGENpro server (http:// scrat ch. Prote omics. ics. uci. edu/) is based on data collected from microarray analysis using machine learning algorithms. The input of the constructed mRNA vaccine includes only the amino acid sequence of a translated form of the ORF while excluding tPA and MITD sequences. Allergenicity of the construct was tested using AllerTOP 2.0 server 53 , while the toxicity of the vaccine was predicted using ToxinPred server 54 . Ultimately, the online webserver ProtParam (https:// web. expasy. org/ protp aram/) was used to predict various physicochemical properties of the vaccine. These characteristics include the composition of amino acid, molecular weight, theoretical Isoelectric point (pI), Instability Index (II), Aliphatic Index (AI), and Grand Average of Hydropathicity (GRAVY) 89 .
In silico immune simulation. The C-ImmSim, an online simulation server (http:// 150. 146.2. 1/C-IMMSIM/ index. php), was used to perform a dynamic simulation of immune response for the vaccine construct while setting all criteria to default 73 . Its mode of action is based on epitopes in conjunction with lymphocyte receptors and hence simulates the immune response. Giving 2-3 doses within four weeks is recommended for most current vaccines. Hence, we used three doses of 1000 vaccine units over four weeks in the immune simulation of this study 90 . We set all parameters to default and three injections at time-step 1, 84, and 168, respectively. Codon Optimization of the vaccine construct. The peptide vaccine construct needs to undergo a codon optimization for efficient expression within the human cells. Accordingly, we used the GenSmart Codon Optimization Tool (http:// www. gensc ript. com/) by GenScript (GS). Quality assessment of the optimized sequence was performed using the Rare Codon Analysis tools (http:// www. gensc ript. com/) by GenScript (GS). The efficiency of translation of the mRNA is expressed as Codon Adaptation Index (CAI). The existence of any tandem unusual codons is indicated as Codon Frequency Distribution (CFD). Secondary Structure Prediction of the mRNA vaccine. The secondary structure of the mRNA vaccine was projected using the RNAfold tool (http:// rna. tbi. univie. ac. at/ cgi-bin/ RNAWe bSuite/ RNAfo ld. cgi) of ViennaRNA Package 2.0. It uses McCaskill's algorithm to compute the predicted secondary structure's minimal free energy (MFE). This tool measured the minimal free energy (MFE) structure and the centroid secondary structure and their minimum free energy.
Prediction and validation of secondary and 3D structures of the vaccine peptide. Excluding the tPA signal and MITD sequences, the PSIPRED server (http:// bioinf. cs. ucl. ac. uk/ psipr ed/) was used to predict the peptide's secondary structure based on position-specific scoring matrices with an accuracy of 84.2%. The Robetta server (https:// robet ta. baker lab. org/) was used to predict five possible three-dimensional structures of a peptide sequence 91 . The ProSA-web (https:// prosa. servi ces. came. sbg. ac. at/ prosa. php), PROCHECK, and ERRAT (https:// saves. mbi. ucla. edu/) were used to confirm the best structure.
Prediction of conformational B-cell epitopes. The tertiary structure of the protein can induce new conformational B-cell epitopes. ElliPro, an online server (http:// tools. iedb. org/ ellip ro/), has been used to predict